Dialogical Reasoning Across AI Architectures: A Multi-Model Framework for Testing AI Alignment Strategies
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
This paper introduces a methodological framework for empirically testing AI alignment strategies through structured multi-model dialogue. Drawing on Peace Studies traditions - particularly interest-based negotiation, conflict transformation, and commons governance - we operationalize Viral Collaborative Wisdom (VCW), an approach that reframes alignment from a control problem to a relationship problem developed through dialogical reasoning. Our experimental design assigns four distinct roles (Proposer, Responder, Monitor, Translator) to different AI systems across six conditions, testing whether current large language models can engage substantively with complex alignment frameworks. Using Claude, Gemini, and GPT-4o, we conducted 72 dialogue turns totaling 576,822 characters of structured exchange. Results demonstrate that AI systems can engage meaningfully with Peace Studies concepts, surface complementary objections from different architectural perspectives, and generate emergent insights not present in initial framings - including the novel synthesis of "VCW as transitional framework." Cross-architecture patterns reveal that different models foreground different concerns: Claude emphasized verification challenges, Gemini focused on bias and scalability, and GPT-4o highlighted implementation barriers. The framework provides researchers with replicable methods for stress-testing alignment proposals before implementation, while the findings offer preliminary evidence about AI capacity for the kind of dialogical reasoning VCW proposes. We discuss limitations, including the observation that dialogues engaged more with process elements than with foundational claims about AI nature, and outline directions for future research including human-AI hybrid protocols and extended dialogue studies.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 8 of its 11 references that are in the map. First posted on arXiv 28 Jan 2026.
Builds on
8 of 11 references.
Closest ancestors · 1
Same direction, newest first
Other references · 7
- 2017Deep reinforcement learning from human preferencescited by 3,846
- 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debatecited by 995
- 2020Artificial Intelligence, Values and Alignmentcited by 443
- 2024Debating with More Persuasive LLMs Leads to More Truthful Answerscited by 255
- 2023Evaluating Large Language Models in Theory of Mind Taskscited by 217
- 2020Open Problems in Cooperative AIcited by 167
- 2022Aligned with Whom? Direct and social goals for AI systemscited by 7
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025Normative Conflicts and Shallow AI Alignment
- 2025Collaborate, Deliberate, Evaluate: How LLM Alignment Affects Coordinated Multi-Agent Outcomes
- 2026LCAM: A Framework for Diagnosing Interactional Alignment Failures in Con-versational AI
- 2026Emergent Collaborative Deliberation in Multi-Model AI Systems: A BFT-Derived Protocol for Epistemic Synthesis
- 2025SynLang and Symbiotic Epistemology: A Manifesto for Conscious Human-AI Collaboration
- 2025PRISM: Perspective Reasoning for Integrated Synthesis and Mediation as a Multi-Perspective Framework for AI Alignment
- 2024The AI Alignment Paradox
- 2024AI Alignment through Reinforcement Learning from Human Feedback? Contradictions and Limitations